Blaise Agüera y Arcas: evaluation

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…very computational and then a sudden transition takes place here at 6,000,000 interactions and it becomes intensely computational. It looks like a phase transition. In fact, it is a phase transition. You can also see that in the the entropy of the soup. So here I'm just I'm just estimating the entropy of the soup by zipping it and looking at the size of the zip relative to the, to the whole thing. You can use any compression algorithm you like. In the beginning, it's uncompressible, so it's a gas, you know, in that Turing gas sense because all the bytes are random. And you can see that there's a dramatic change and suddenly it becomes extremely compressible right at that transition moment. And of course, this becomes compressible because there everything is copying, right, itself and each other, so if things are copying themselves, then they'll then we know that they'll become very compressible. But it's cool because if we think about what the phase of matter is on the left, it is just like a gas, nothing is correlated. What would we call the phase of matter on the right? It's not a liquid, it's not a solid. Right? It has structure and it has structure at every scale. I think you have to call that phase of matter life. That's it's a functional phase of matter. It means that that it it that its parts are different from its other parts and if you zoom in or out, you see more structure. It's what David Wolpert would call self dissimilar. It's not a fractal, it's more like a multifractal. I'll explain why in a moment. Okay. How long does it take this transition to happen? Well, the the answer is it looks more or less like an Erlang distribution or, a little bit more precisely like this distribution they call a lockpick distribution, which imagines that there are steps have to be undertaken and those steps have a long tail distribution of difficulty. And how many steps does it take? Well, the answer is 12. It takes 12 steps. Just like getting sober, I suppose. This is a fit of the empirical to the Erlang and the lockpick distributions. A little hard to see, but the lockpick is a bit better than Erlang. Erlang assumes Poisson, lockpick assumes long tailed. But it's a process phase distribution. And and what this tells you is that there are stepping stones here. You know, you can't get that transition to life immediately, so something interesting must be going on here on the left other than just randomness. It takes multiple things happening in order to get to that point. You know, in this case, you know, it happens somewhere between 1,000,000 and, let's say, 7,000,000, interactions. Okay. So, this all suggests that pretty much any universe, by the way, that has a source of randomness, and can support computation, will evolve life, you know, for this simple dynamical stability reason. But the big mystery is why does why does it appear to continue to get more complex over time? You might have seen my little video that, you know, we saw some programs emerge and then we saw the pro we saw them sort of densify. More instructions appeared. And and even more fundamentally, why does this work even without mutation? that, you know, we saw some programs emerge and then we saw the pro we saw them sort of densify. More instructions appeared. And and even more fundamentally, why does this work even without mutation? I didn't mention, but, you know, in the original version of BFF, I added some random mutation because, you know, we're all taught in school that the way the way evolution works is chance and necessity. You know, you mutate things, you're sort of throwing spaghetti at the wall. Whatever sticks is what is what does better, and so you need a source of spaghetti. But if you do this entire experiment with the mutation rate cranked all the way down to 0, you still get the same exact phenomenon, and that is very mysterious. Because if you crank mutation down to 0, you should have no source of novelty, you should have no evolution. Why do you still get this apparent complexification even with 0 mutation? So let's let's go into some of the some of the theory of this. By the end, we have a replicating entity. It can engage in standard sort of population evolution dynamics. This is the kind of of differential equation that that 1 generally writes for this sort of thing. It's a very general ansatz. This is for, you know, species I. Let's say they're n species. They could be chemical species, they could be biological species, whatever. Here's a classic example of of such an ansatz. This is the the Lotka Volterra equations for predator and prey, which I'm sure, many of you are very familiar with. They were co invented or or invented independently by, Alfred Lotka and Vito Volterra near the beginning of the twentieth century. This is what the classic Lotka Volterra equations look like. There are 2 species. There is a prey species and a predator species. And those 4 terms are, reproduction, getting eaten, eating to reproduce and background death rate. So, if you've got those 4 terms, you get these nice oscillatory solutions, you know, between between your predators and your prey that arise. Okay. So this is a slightly more general form of those Lotka Volterra equations. There is a linear part which we'll call r x and, in Lotka Volterra that linear part is diagonal. So, you know, the the, the wolf can't turn into a rabbit, the rabbit can't turn into a wolf, so so the reproduction is diagonal. And then there's also a bilinear term, which is the the part where predation, competition and the fact that niches are finite, gets implemented. So the the the right part is suppressive. The left part makes things grow. The right part makes things, squish squish down, keeps them finite. But this can't be the whole story of evolution. Why can't it be the whole story of evolution? Well, of course, because it's closed ended. You know, we only have 2 species here. It doesn't matter how long you run this damn thing, you're not gonna get a third species. And and you're not going to change the design space either. You can have, you know, very complicated terms in here that allow finch beaks to adapt to different environments, but you have to have the space of finch beaks predefined before before this equation can even be made to work. very complicated terms in here that allow finch beaks to adapt to different environments, but you have to have the space of finch beaks predefined before before this equation can even be made to work. So this doesn't, you know, this doesn't answer the question of how evolution gets started. It doesn't answer the question of what happens afterward other than optimization to niches. So now we bring in another Eastern European, Dmitry Sergeyevich Mereshkovsky. So he's the 1 who first came up with the idea that maybe mitochondria engaged in some kind of semogenetic event in order to end up inside other single celled organisms to make, to make eukaryotes. This was popularised and proven to actually be the case by Lin Margulis, in in 1968. 1 of the really great papers in biology from the twentieth from the twentieth century. I'm sure many of you are familiar with. This is that paper, sorry, 1966 on the origin of mitosing cells. So she's the 1 who proved, that eukaryotes were actually a fusion between 2 different kinds of prokaryotes and popularized this term that Medichevsky had invented, symbiogenesis. Okay. So could symbiogenesis be happening as a as a source of novelty in BFF? Yes. That is the source of novelty in BFF and indeed that is the source of novelty in evolution period. This is something that Lyn Margulis believed, but, that was not, that had not been widely accepted by the by the by the biology community even by the time of her death, in 2011. You know, so she she had a much more expansive idea about about why symbiogenesis was important. Only the particulars of chloroplasts and mitochondria had been accepted. So the way we can look for symbiogenesis in BFF is to look for replicators emerging before that phase transition. And if you look for them, if you just look for stretches of bytes that are getting copied during those interactions, you find such stretches of bytes. They begin short and kind of crappy, unreliable. But they're there from the beginning. Every time you have a single copy instruction after all, 1 byte is getting copied from somewhere to somewhere, so almost by definition, you have at least 1 byte long sequences that are getting copied right from the beginning. So let's just call them replicators. Right? There are replicators there from the beginning. Now, if you have these 1 byte replicators that are copying themselves back and forth now and then, once in a while, they will come into conjunction, and 2 of them will copy better as a group than the 2 of them copied on their own. When that happens, then they'll start to copy as a group and that is a symbiogenetic event. So basically, the reason that even without mutation you get these complex programs arising is because of these fusion events between smaller replicators. So can 1 build syngogenesis into an equation like like this 1 that, you know, for for Lotka Volterra? You can. This is our statistical physicist who came up with the right kind of term to write mathematically for describing how symbiogenesis works. He wrote down an equation for the coagulation of polymers. So this is Smolowski coagulation. This is what happens when clouds form.…

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